Make is a visual workflow automation platform that connects over 3,000 apps and services, enabling users to build automated workflows without extensive coding
Operations and marketing teams automating multi-step, cross-app processes
No-code and low-code builders who want visual control over complex logic
Businesses running high-volume workflows sensitive to per-operation cost
Pros
Auto-generates web UIs and webhooks from your scripts, with no frontend work required
Polyglot: Python, TypeScript, Go, Bash, SQL, PowerShell, Rust, and more in one platform
Many triggers (schedules, webhooks, HTTP routes, Kafka, WebSockets, email) plus a flow and low-code app builder
Fast, self-hostable engine with a Rust backend, Postgres queue, and nsjail sandboxing; deploys via Docker Compose or Kubernetes
Free for internal use, with Git sync, a VS Code extension, and a community hub of shared scripts
The visual scenario builder makes complex, multi-branch automations far easier to design and audit than linear step-based tools, with data flow and logic represented clearly on a single canvas.
A library of 3
000+ pre-built app connectors, backed by generic HTTP, webhook, and custom app modules, means nearly any API can be reached even when a native integration is missing.
Make is genuinely AI-native, offering agentic automation, ready-made AI agents, and an MCP server that lets AI assistants trigger real actions across connected apps rather than just generating text.
The pricing is competitive for high-volume workflows, with an operations-heavy allowance at entry-level tiers and a no-time-limit free plan for experimentation.
Cons
AGPL-3.0 plus proprietary Community Edition features: commercial redistribution, embedding, or managed-service use requires a paid license
Self-hosting requires running and maintaining Postgres and worker infrastructure
The project states it is not currently seeking outside contributions, limiting community code input
The breadth of triggers, flows, and apps carries a learning curve for new teams
The visual canvas is powerful but has a real learning curve; newcomers to automation often find its data mapping, iterators, and error handling harder to grasp than simpler competitors.
The recently introduced credit-based billing model can make costs harder to predict, since advanced and AI-powered actions may consume more credits than a standard operation.
Complex scenarios can become difficult to maintain and debug at scale, and heavy reliance on the platform creates a degree of workflow lock-in.
There is no self-hosted or offline option, so teams with strict on-premise or air-gapped requirements will need an alternative.
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